1 About SSES

SSES is an R package that contains code for specifying and running a landscape scale social ecosystems model (Carruthers et al. 2018). The package contains in-line help for specifying landscape models and running analyses.

The default landscape object is for the BC Trout fishery that includes 584 stocked lakes and 9 population centres each with four angler classes.

The model is also represented in an SSES Shiny App that is available both locally (is in the R package) and online.

The SSES App will function on low resolution monitors but, due to interacting with a high resolution map, works best on 1080p monitors or preferably higher. As with all html webpages, if you run into issues with the layout, try zooming in/out using ctrl-mouse wheel or ctrl +/-.

2 Purpose of this document.

This document:

  • Provides a brief background to Spatial Social-Ecological Systems (SSES) modelling
  • Describes the default BC Trout lakes example.
  • Provides guidance on installing the SSES R package and running the SSES Shiny App locally and online
  • Explains how to use the SSES Shiny App

3 Quick Start

To take a quick first look at the SSES App follow this link

4 Introduction

4.1 Social Ecological Systems Models

4.2 The BC Rainbow Trout SSES

5 Accessing the App

5.1 Online

SSES is freely available online.

5.2 Offline

You can also run the App locally on your computer. To do so install the R package and use the SLICK() function:

library(devtools)
install_github("blue-matter/SSES")
library(SSES)
Shiny("SSES")

6 Using the App

The process for using the App follows the steps:

  • Step 1. Select lakes (Ext. Selection tab)

  • Step 2. Change management (Regulations and/or Stocking tabs)

  • Step 3. Recalculate landscape effort (Recalculation button)

  • Step 4. Compare outcomes (Outcomes)

  • Step 5. Create a new management scenario (Options),

  • Repeat steps 1-5 for a new management scenario and so on.

Selection occurs on the left-hand side and is facilitated by a navigable map. Specification of alternative management scenarios, graphing of outputs etc is carried out using the tabs on the right-hand side of the App:

6.1 Landscape Info Tab

6.1.1 Lake attributes

6.1.2 Management - all lakes / selected lakes

6.2 Extended Selection Tab

6.3 Regulations Tab

6.3.1 Edit Regulations by Individual Lake

6.3.2 Edit Regulations by Selected Lakes

6.4 Stocking Tab

Six stocking types are modelled in the B.C. trout lakes landscape:

6.4.1 Edit Stocking by Individual Lake

6.4.2 Edit Stocking by Selected Lakes

6.5 Projection of trade-off

6.6 State Projection 1

In many decision makings context there is a state variable of interest (e.g. population numbers) that like performance metrics has a projected future. Unlike performance metrics, state variables also have a historical reconstruction that provides important context for projected outcomes.

6.7 Performance Trade-offs 1

6.8 Performance Trade-offs 2

6.9 Performance Trade-offs 3

6.10 Performance Trade-offs 4

6.11 Performance Comparison 5

6.12 State Projection 2

7 Loading SLICK examples

8 Acknowledgements

Blue Matter

9 References

Butterworth, D.S., Punt, A.E. 1999 Experiences in the evaluation and implementation of management procedures. ICES Journal of Marine Science, 5: 985-998, http://dx.doi.org/10.1006/jmsc.1999.0532.

Cochrane, K L., Butterworth, D.S., De Oliveira, J.A.A., Roel, B.A., 1998. Management procedures in a fishery based on highly variable stocks and with conflicting objectives: experiences in the South African pelagic fishery. Rev. Fish. Biol. Fisher. 8, 177-214.

Punt, A.E., Butterworth, D.S., de Moor, C.L., De Oliveira, J.A.A., Haddon, M. 2014. Management strategy evaluation: best practices. Fish and Fisheries. 17(2): 303:334.

10 Glossary